Product & Engineering Teams
Make AI load-bearing.
SkipForward embeds engineers with the product and platform teams responsible for shipping AI features, and takes them from a convincing demo to something users depend on — evaluated, guardrailed, and cheap enough to leave running.
What shipping actually looks like
Typical first engagement- Demo to production
- 6 weeks
- Cost per task
- −62%
- Critical paths under eval
- 100%
We start with one feature, take the baseline with your team, and put a date on it. If the number does not move, neither does the invoice.
The Gap
Everyone shipped an AI feature. Almost nobody shipped a dependable one.
Your team already built the demo. It worked in the standup, it worked in the exec review, and it works about eighty percent of the time — which is the exact number that makes a feature impossible to put in front of a customer.
The distance between a convincing demo and a dependable product is not model quality. It is evals, retrieval, fallbacks, latency budgets, cost per task, the permission model, and enough observability to know which of those broke at two in the morning.
That is where SkipForward comes in.
Nobody churns because your AI feature is unimpressive. They churn because it was wrong once about something that mattered.
What We Build
The unglamorous half.
The demo is the easy part, and your team already did it. We build the scaffolding that turns it into a feature you can leave on.
Features on the critical path
Not a sidebar assistant nobody opens. The thing in the middle of the product your users would notice immediately if it stopped working.
Evals before opinions
Task-specific eval suites and regression gates in CI, so a prompt or model change ships on evidence instead of on whoever demoed last.
Retrieval that respects permissions
Extraction, chunking, freshness, and per-user authorization — so the system never answers with something the person asking should not see.
Latency and cost budgets
A per-task budget set with your team, then the routing, caching, and model right-sizing needed to stay inside it at real volume.
Observability for non-deterministic systems
Traces, output sampling, drift alerts, and a way to answer why it said that three weeks after it said it.
Your team owns it
Pairing, code review, and internal workshops until your engineers ship the next AI feature without us on the call.
Model Strategy
The model is an implementation detail.
We start on whatever proves the outcome fastest, which is usually a frontier API. If cost, latency, or residency later justify moving to a smaller open model we fine-tune and you own, we do that work too. Plenty of engagements never need it — and we say so rather than sell it.
Worth right-sizing when
- Inference spend is material at production volume
- Latency has to fit inside a live workflow
- Data residency rules out a third-party API
Not worth it when
- Volume is low enough that API pricing wins
- The task needs frontier-level breadth or reasoning
- Your team would rather not operate GPUs
How We Work
Forward deployed, then gone.
A small team of engineers deployed into your organization, shipping in your environment on a clock we set together — with the handoff planned from the first week.
Phase 01
Embed
Our engineers sit with your team, map the workflow end to end, and agree on the one number we are here to move.
Phase 02
Prototype
A working system against your real data, in your environment. Narrow scope, honest evals, no demo-ware.
Phase 03
Productionize
Guardrails, regression evals, monitoring, and CI. It runs on a Tuesday morning without anyone watching it.
Phase 04
Transfer
Documentation, pairing, and training until your engineers own the roadmap. We stay on call, not on payroll.
Four phases, no fixed calendar. Each phase is sized to your scope rather than to a template. One well-bounded workflow can run embed to transfer in about three weeks; several systems in a regulated environment take longer. We size the phases with you during Phase 01, and the dates go in the contract before anyone writes code.
Fixed-scope pilot
One outcome, one price, agreed before we start.
Your stack, your cloud
We build in your repos and your accounts from day one.
Handoff in the contract
Exit criteria are written down, not left to good intentions.
Start Here
Let’s find your first outcome.
Thirty minutes to walk through the workflow you want to change. You leave with a one-page scope, a measurable target, and a price — whether or not you hire us.